小波神经网络在电子设备故障诊断中的应用
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摘要
本课题是东北大学设备诊断工程中心与军队针对防化车电路板故障诊断的实际需求,联合研发的一个实际项目的深入研究。
模拟电路故障诊断是一个十分必要且有意义的课题。本文把小波神经网络的算法应用在模拟电路故障诊断中,并做了讨论和研究,在模拟电路下对该算法进行了仿真,取得了较好的诊断效果。
小波神经网络是小波分析理论与人工神经网络理论结合的产物,它兼容了小波与神经网络的优越性,一方面,充分利用了小波变换的时频局部化特性；另一方面,充分发挥了神经网络的自学习能力,从而具有较强的逼近与容错能力。由于其优越的特性,小波神经网络被广泛用于信号处理、函数拟合、数据预测、系统辨识、故障诊断和自动控制等多个方面。
应用LABVIEW及MATLAB软件设计了小波神经网络的故障诊断程序,对进行仿真实验,并与普通的BP网络算法对比,证明了小波神经网络可以并更适合处理防化车模拟电路故障。
本文在实际项目的基础上,取得的了一些成果,但还存在一些需要解决的问题及难点,望以后能一一得到解决。
This is a practical subject co-developed by Northeastern University Equipment Diagnosis Center and military Region to meet the need of reality.
In this paper, an algorithm using wavelet neural network for fault diagnosis in analog circuit witholerance is proposed, and is simulated on the direct circuit and the alternating circuit andreceives better result.
Wavelet neural network is the combination of Wavelet Analysistheory and Artificial Neural Networks theory. It not only possesses the wavelet transform's time-frequency localizationability, but also makes full use of the property of self-learning of Artificial Neural Networks, which enables Wavelet neural network to have better ability of approximation and better fault-tolerance capacity. Because of its particular merits, Wavelet neural network is widely used in many fields, such as signal processing, functionapproximation, data forecast, system identification and patternrec-ognition.
At last, corresponding programs are designed for training the general BP neural network and wavelet neural network by MATLAB, LABVIEW and simulation analysis proved that wavelet neuralnetwork is adaptive in complex pattern classifying.
In this paper, some results are obtained, but there is also some difference from thepractical application, and it still should be studied in the future.
模拟电路故障诊断是一个十分必要且有意义的课题。本文把小波神经网络的算法应用在模拟电路故障诊断中,并做了讨论和研究,在模拟电路下对该算法进行了仿真,取得了较好的诊断效果。
小波神经网络是小波分析理论与人工神经网络理论结合的产物,它兼容了小波与神经网络的优越性,一方面,充分利用了小波变换的时频局部化特性；另一方面,充分发挥了神经网络的自学习能力,从而具有较强的逼近与容错能力。由于其优越的特性,小波神经网络被广泛用于信号处理、函数拟合、数据预测、系统辨识、故障诊断和自动控制等多个方面。
应用LABVIEW及MATLAB软件设计了小波神经网络的故障诊断程序,对进行仿真实验,并与普通的BP网络算法对比,证明了小波神经网络可以并更适合处理防化车模拟电路故障。
本文在实际项目的基础上,取得的了一些成果,但还存在一些需要解决的问题及难点,望以后能一一得到解决。
This is a practical subject co-developed by Northeastern University Equipment Diagnosis Center and military Region to meet the need of reality.
In this paper, an algorithm using wavelet neural network for fault diagnosis in analog circuit witholerance is proposed, and is simulated on the direct circuit and the alternating circuit andreceives better result.
Wavelet neural network is the combination of Wavelet Analysistheory and Artificial Neural Networks theory. It not only possesses the wavelet transform's time-frequency localizationability, but also makes full use of the property of self-learning of Artificial Neural Networks, which enables Wavelet neural network to have better ability of approximation and better fault-tolerance capacity. Because of its particular merits, Wavelet neural network is widely used in many fields, such as signal processing, functionapproximation, data forecast, system identification and patternrec-ognition.
At last, corresponding programs are designed for training the general BP neural network and wavelet neural network by MATLAB, LABVIEW and simulation analysis proved that wavelet neuralnetwork is adaptive in complex pattern classifying.
In this paper, some results are obtained, but there is also some difference from thepractical application, and it still should be studied in the future.
引文
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4.黄洁,何怡刚.模拟电路故障诊断的发展与展望[J],微电子学.2004,34(1)：21-25
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41. Jiang Q. On the Design of Multifilter Banks and Orthonormal Multiwavelets Banks[J], IEEE Trans. On SP. December 1998, Vol.46, No.12:3292-3303
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2.赵建.模拟电路故障诊断的研究[J],上海海运学院学报.2002,9(3)：48-55
3.蔡金锭,马西奎,黄东泉.容差网络故障的区间诊断法[J],小型微型计算机系统.2000.21(11)：1190-1192
4.黄洁,何怡刚.模拟电路故障诊断的发展与展望[J],微电子学.2004,34(1)：21-25
5.邹锐.模拟电路故障诊断原理和方法[M],武汉：华中理工大学出版社,1988,10-15
6. Ozawa T. Analog Methods for Computer-Aided Circuit Analysis and Diagno sis[J], NewYork,1988,50-60
7.孙义闯.模拟电路故障诊断理论与方法综述[J],大连海运学院,2002,9：45-50
8.谭阳红,何怡刚.模拟电路故障诊断的新故障字典法[J],微电学,2001：252-254
9. D.H.Zhou, Y.Z.Ye. Modern Fault Diagnosis and Fault Tolerant Control[M], Beijing:Tsing-hua University Press,2000,144-149
10. C K Chui. An Introduction to Wavelets[M], Academic Press,1992,31-43
11.孙增圻,张再兴,邓志东.智能控制理论与技术[M],北京：清华大学出版社,广西科学技术出版社,1997,100-105
12. Zhang Qinghua and Benvenist Albert. Wavelet networks[J], IEEE Trans On Neural Network,1992,3(6):889-898
13.李伯权,王翔.小波包分析在变速箱故障自动诊断中的应用[J],江苏大学学报,2003,02：23-31
14.谢宏,何怡刚等.小波神经网络在模拟电路故障诊断中的应用研究[J],湖南大学学报(自然科学版)2004,31(4)：38-40
15.黄天戍,马世纪,罗志华.基于小波网络的水轮机组故障诊断[J],中国仪器仪表2004,04：33-36
16.周刚,张大发,蔡章生.人工神经网络理论在核动力领域的应用与展望[J],核技术,2004,03：12-14
17.冉启文.小波变换与分数傅里叶变换理论及应用[M],哈尔滨工业大学出版社,2001,89-90
18.孙延奎.小波分析及应用[M],北京：机械工业出版社,2005,57-159
19.唐晓初.小波分析及应用[M],重庆：重庆大学出版社,2006,1-10
20.崔锦泰著,程正兴译.小波分析导论[M],西安交通大学出版社,1997,68-69
21.秦前清,杨宗凯.实用小波分析[M],西安电子科技大学出版社,1998,70-72
22. DAUBECHIES I. The wavelet transform, time-frequency location and signal analysis[J], IEEE Trans. on Information Theory,1990,36(5):961-100
23. Mallat S, Hwang W L. Singularity detection and processing with wavelets [J], IEEE Trans, On Information Theory,1992,38(2):617-643
24. Dexian Huang, Yihui Jin. The Application of Wavelet Neural Networks to Nonlinear Predictive Control[J], Proceedings of the 1997 IEEE International Conference on Neural Networks, Part 2(of4).1997,724-727
25. J.R.Hull, et al. A Neural Network Algorithm Using Wavelets and Auto Regr essive Inputs for System Identification of the 1997[J], IEEE International Co nference on Networks,1997,2(4):724-727
26. H.Szu, T.Brian. Neural Network Adaptive Wavelets for Signal Repre sentati on and Classification[J], Optical Engineering,199231(9):1907-1916
27. H.H.Szu, Shubha Kadambe. Neural network adaptive wavelets for sianal repr esentation andclassification[J], Optical Engineering,1992,31(9):1907-1919
28.秦前清,杨宗凯.实用小波分析[M],西安：西安电子科技大学出版社,1998,56-57
29.崔锦泰(著),程正兴(译).小波分析导论[M],西安交通大学出版社,1995
30. Mallat S. A Theory for Multiresolution Signal Decomposition:the Wavelet Representation[J], IEEE Trans, on PAMI,1989, Vol.11, No.7:674-693
31. Mallat S. Multiresolution Approximations and Wavelet Orthonormal Bases of L2(R)[J], Trans. Amer. Math. Soc,1989, Vol.315:69-87
32. Mallat S. Multifrequency Channel Decomposition of Images and Wavelet [J], IEEE Trans. on ASSP,1989.12, Vol.37:2091-2110
33. Sweldens W. The Lifting Scheme:A Construction of Second Generation W avelets[J], SIAM J.Math.Anal.,1997, Vol.29, No.2:511-546
34. Cohen A, Daubechies I. and Vial P. Wavelets on the Interval and Fast Wav elet Transform[J], Appl.Comp.Harm.Anal,1993, Vol.1:54-80
35. Donoho D.L. Interpolating Wavelet Transform[J], echnical Report, Dept. of Statistics Stanford Univ,1992.10:56-76
36. Dubuc S. Interpolating Through an Iterative Scheme[J], J.Math.Anal. and Appl,1986, Vol.114:185-204
37.郑小洋.小波神经网络及其应用[D],重庆大学工学硕士学位论,2003
38. Rioul O. and Vellerli M. Wavelets and Signal Processing[J], IEEE Signal Pr ocessingMagzine,1991, Vol.8, No.4:14-38
39. Donoho D.L. and Jonestone I. Ideal Spatial Adaptation via Wavelet Shrinkag e[J], Biometrika, December 1994, Vol.81:425-455
40. Chui C.K. and Lian J. A Study of Orthonormal Multi-Wavelets[J], Appl. Nu mer. Math.1996, Vol.20, No.3:273-298
41. Jiang Q. On the Design of Multifilter Banks and Orthonormal Multiwavelets Banks[J], IEEE Trans. On SP. December 1998, Vol.46, No.12:3292-3303
42. Candes E J. Ridgelets:Theory and Applications[J], Ph.D. Thesis, Department of Statistics, Stanford University,1998
43. Daubechies I. Orthonormal Bases of Compactly Supported Wavelets[J],Comm unPure Appl Math.1988, Vol.41:909-996
44. P. G. Lisboa(英)著.刑春颖,阳影译.现代神经网络应用[M],北京：电子工业出版社,1996,341-342
45.周志华,曹存根.神经网络及其应用[M],北京：清华大学出版杜,2004,1-10
46.何怡刚,罗先觉.基于神经网络的线性电路故障诊断的非线性L1范数优化方法[J],电子测量与仪器学报,1998,(1)：18-22
47.党建武.神经网络技术及应用[M],中国铁道出版社,2000,120-121
48. C.C.Wu. Analog Fault Diagnosis with Failure Bounds[J], IEEE Trans.On Cir cuits andSyst.Vol.CAS-29,1982, (5):227-284
49. Bandler J W, Salama A E.Fault Diagnosis of Analog Circuits[J],Proceeding s of theIEEE,2002,73(8):1279-1327
50. Salama A E, Bandler J W.A Unified Decomposition Approach for Fault Loc ation in Large Analog Circuits[J], IEEE Trans.on CAS,2004,31:609-622
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